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Poster
Torsional Diffusion for Molecular Conformer Generation
Bowen Jing · Gabriele Corso · Jeffrey Chang · Regina Barzilay · Tommi Jaakkola

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #438
in Poster Session 3 »

Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a novel diffusion framework that operates on the space of torsion angles via a diffusion process on the hypertorus and an extrinsic-to-intrinsic score model. On a standard benchmark of drug-like molecules, torsional diffusion generates superior conformer ensembles compared to machine learning and cheminformatics methods in terms of both RMSD and chemical properties, and is orders of magnitude faster than previous diffusion-based models. Moreover, our model provides exact likelihoods, which we employ to build the first generalizable Boltzmann generator. Code is available at https://github.com/gcorso/torsional-diffusion.

Keywords: Equivariance · Geometric Deep Learning · diffusion models · molecular structure · score-based models · Boltzmann generator · conformer generation
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Bowen Jing (Massachusetts Institute of Technology)
Gabriele Corso (MIT)
Jeffrey Chang (Dept of Physics, Harvard University)
Regina Barzilay (Massachusetts Institute of Technology)
Tommi Jaakkola (MIT)

Tommi Jaakkola is a professor of Electrical Engineering and Computer Science at MIT. He received an M.Sc. degree in theoretical physics from Helsinki University of Technology, and Ph.D. from MIT in computational neuroscience. Following a Sloan postdoctoral fellowship in computational molecular biology, he joined the MIT faculty in 1998. His research interests include statistical inference, graphical models, and large scale modern estimation problems with predominantly incomplete data.

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